def testPathsNotInvalidatedByOtherDestroys(self):
""" Creates segments and synapses, then destroys segments and synapses on
either side of them and verifies that existing Segment and Synapse
instances still point to the same segment / synapse as before.
"""
connections = Connections(1024)
segment1 = connections.createSegment(11)
connections.createSegment(12)
segment3 = connections.createSegment(13)
connections.createSegment(14)
segment5 = connections.createSegment(15)
synapse1 = connections.createSynapse(segment3, 201, .85)
synapse2 = connections.createSynapse(segment3, 202, .85)
synapse3 = connections.createSynapse(segment3, 203, .85)
synapse4 = connections.createSynapse(segment3, 204, .85)
synapse5 = connections.createSynapse(segment3, 205, .85)
self.assertEqual(203, synapse3.presynapticCell)
connections.destroySynapse(synapse1)
self.assertEqual(203, synapse3.presynapticCell)
connections.destroySynapse(synapse5)
self.assertEqual(203, synapse3.presynapticCell)
connections.destroySegment(segment1)
self.assertEqual([synapse2, synapse3, synapse4],
list(connections.synapsesForSegment(segment3)))
connections.destroySegment(segment5)
self.assertEqual([synapse2, synapse3, synapse4],
list(connections.synapsesForSegment(segment3)))
self.assertEqual(203, synapse3.presynapticCell)
def __init__(self,
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
seed=42):
"""
Translate parameters and initialize member variables
specific to TemporalMemory
"""
numberOfCols = 1
for n in columnDimensions:
numberOfCols *= n
super(TemporalMemoryShim,
self).__init__(numberOfCols=numberOfCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPermanence,
connectedPerm=connectedPermanence,
minThreshold=minThreshold,
newSynapseCount=maxNewSynapseCount,
permanenceInc=permanenceIncrement,
permanenceDec=permanenceDecrement,
permanenceMax=1.0,
globalDecay=0,
activationThreshold=activationThreshold,
seed=seed)
self.connections = Connections(numberOfCols * cellsPerColumn)
self.predictiveCells = set()
def __init__(self,
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
predictedSegmentDecrement=0.0,
seed=42):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected synapses on a segment is at least this threshold, the segment is said to be active.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse is greater than this value, it is said to be connected.
@param minThreshold (int) If the number of synapses active on a segment is at least this threshold, it is selected as the best matching cell in a bursting column.
@param maxNewSynapseCount (int) The maximum number of synapses added to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses are decremented during learning.
@param predictedSegmentDecrement (float) Amount by which active permanences of synapses of previously predicted but inactive segments are decremented.
@param seed (int) Seed for the random number generator.
"""
# Error checking
if not len(columnDimensions):
raise ValueError(
"Number of column dimensions must be greater than 0")
if not cellsPerColumn > 0:
raise ValueError(
"Number of cells per column must be greater than 0")
# TODO: Validate all parameters (and add validation tests)
# Save member variables
self.columnDimensions = columnDimensions
self.cellsPerColumn = cellsPerColumn
self.activationThreshold = activationThreshold
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
self.predictedSegmentDecrement = predictedSegmentDecrement
# Initialize member variables
self.connections = Connections(self.numberOfCells())
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
self.matchingSegments = set()
self.matchingCells = set()
def connectionsFactory(*args, **kwargs):
""" Create a Connections instance. TemporalMemory subclasses may override
this method to choose a different Connections implementation, or to augment
the instance otherwise returned by the default Connections implementation.
See Connections for constructor signature and usage
@return: Connections instance
"""
return Connections(*args, **kwargs)
def testComputeActivity(self):
""" Creates a sample set of connections, and makes sure that computing the
activity for a collection of cells with no activity returns the right
activity data.
"""
connections = Connections(1024)
# Cell with 1 segment.
# Segment with:
# - 1 connected synapse: active
# - 2 matching synapses
segment1a = connections.createSegment(10)
connections.createSynapse(segment1a, 150, .85)
connections.createSynapse(segment1a, 151, .15)
# Cell with 2 segment.
# Segment with:
# - 2 connected synapse: 2 active
# - 3 matching synapses: 3 active
segment2a = connections.createSegment(20)
connections.createSynapse(segment2a, 80, .85)
connections.createSynapse(segment2a, 81, .85)
synapse = connections.createSynapse(segment2a, 82, .85)
connections.updateSynapsePermanence(synapse, .15)
# Segment with:
# - 2 connected synapses: 1 active, 1 inactive
# - 3 matching synapses: 2 active, 1 inactive
# - 1 non-matching synapse: 1 active
segment2b = connections.createSegment(20)
connections.createSynapse(segment2b, 50, .85)
connections.createSynapse(segment2b, 51, .85)
connections.createSynapse(segment2b, 52, .15)
connections.createSynapse(segment2b, 53, .05)
# Cell with one segment.
# Segment with:
# - 1 non-matching synapse: 1 active
segment3a = connections.createSegment(30)
connections.createSynapse(segment3a, 53, .05)
inputVec = [50, 52, 53, 80, 81, 82, 150, 151]
active, matching = connections.computeActivity(inputVec, .5, 2, .1, 1)
self.assertEqual(1, len(active))
self.assertEqual(segment2a, active[0].segment)
self.assertEqual(2, active[0].overlap)
self.assertEqual(3, len(matching))
self.assertEqual(segment1a, matching[0].segment)
self.assertEqual(2, matching[0].overlap)
self.assertEqual(segment2a, matching[1].segment)
self.assertEqual(3, matching[1].overlap)
self.assertEqual(segment2b, matching[2].segment)
self.assertEqual(2, matching[2].overlap)
def testCreateSegment(self):
connections = Connections(1024)
segment1 = connections.createSegment(10)
self.assertEqual(segment1.cell, 10)
segment2 = connections.createSegment(10)
self.assertEqual(segment2.cell, 10)
self.assertEqual([segment1, segment2],
list(connections.segmentsForCell(10)))
def testUpdateSynapsePermanence(self):
""" Creates a synapse and updates its permanence, and makes sure that its
data was correctly updated.
"""
connections = Connections(1024)
segment = connections.createSegment(10)
synapse = connections.createSynapse(segment, 50, .34)
connections.updateSynapsePermanence(synapse, .21)
synapseData = connections.dataForSynapse(synapse)
self.assertAlmostEqual(synapseData.permanence, .21)
def testReachSegmentLimitMultipleTimes(self):
""" Hit the maxSynapsesPerSegment threshold multiple times. Make sure it
works more than once.
"""
connections = Connections(1024, 2, 2)
segment = connections.createSegment(10)
connections.createSynapse(segment, 201, .85)
self.assertEqual(1, connections.numSynapses())
connections.createSynapse(segment, 202, .9)
self.assertEqual(2, connections.numSynapses())
connections.createSynapse(segment, 203, .8)
self.assertEqual(2, connections.numSynapses())
synapse = connections.createSynapse(segment, 204, .8)
self.assertEqual(2, connections.numSynapses())
def testCreateSegmentReuse(self):
connections = Connections(1024, 2)
segment1 = connections.createSegment(42)
connections.createSynapse(segment1, 1, .5)
connections.createSynapse(segment1, 2, .5)
# Let some time pass.
connections.startNewIteration()
connections.startNewIteration()
connections.startNewIteration()
# Create a segment with 3 synapse.
segment2 = connections.createSegment(42)
connections.createSynapse(segment2, 1, .5)
connections.createSynapse(segment2, 2, .5)
connections.createSynapse(segment2, 3, .5)
connections.startNewIteration()
# Give the first segment some activity.
connections.recordSegmentActivity(segment1)
# Create a new segment with 1 synapse.
segment3 = connections.createSegment(42)
connections.createSynapse(segment3, 1, .5)
segments = connections.segmentsForCell(42)
self.assertEqual(2, len(segments))
# Verify first segment is still there with the same synapses.
self.assertEqual(
set([1, 2]),
set(synapse.presynapticCell
for synapse in connections.synapsesForSegment(segments[0])))
# Verify second segment has been replaced.
self.assertEqual(
set([1]),
set(synapse.presynapticCell
for synapse in connections.synapsesForSegment(segments[1])))
# Verify the flatIdxs were properly reused.
self.assertLess(segment1.flatIdx, 2)
self.assertLess(segment3.flatIdx, 2)
self.assertTrue(
segment1 is connections.segmentForFlatIdx(segment1.flatIdx))
self.assertTrue(
segment3 is connections.segmentForFlatIdx(segment3.flatIdx))
def testCreateSegmentReuse(self):
connections = Connections(1024, 2)
segment1 = connections.createSegment(42)
connections.createSynapse(segment1, 1, .5)
connections.createSynapse(segment1, 2, .5)
connections.computeActivity([], .5, 2, .1, 1)
connections.computeActivity([], .5, 2, .1, 1)
connections.computeActivity([], .5, 2, .1, 1)
segment2 = connections.createSegment(42)
activeSegs, _ = connections.computeActivity([1, 2], .5, 2, .1, 1)
self.assertEqual(1, len(activeSegs))
self.assertEqual(segment1, activeSegs[0].segment)
segment3 = connections.createSegment(42)
self.assertEqual(segment2.idx, segment3.idx)
def __init__(self, learnOnOneCell=True, **kwargs):
"""
@param learnOnOneCell (boolean) If True, the winner cell for each column will be fixed between resets.
"""
super(ExtendedTemporalMemory, self).__init__(**kwargs)
self.activeExternalCells = set()
self.learnOnOneCell = learnOnOneCell
self.chosenCellForColumn = dict()
self.unpredictedActiveColumns = set()
self.predictedActiveCells = set()
self.activeApicalCells = set()
self.apicalConnections = Connections(self.numberOfCells())
self.activeApicalSegments = set()
self.matchingApicalSegments = set()
def testCreateSegmentReuse(self):
connections = Connections(1024, 2)
segment1 = connections.createSegment(42)
connections.createSynapse(segment1, 1, .5)
connections.createSynapse(segment1, 2, .5)
# Let some time pass.
connections.startNewIteration()
connections.startNewIteration()
connections.startNewIteration()
segment2 = connections.createSegment(42)
connections.startNewIteration()
connections.recordSegmentActivity(segment1)
segment3 = connections.createSegment(42)
self.assertEqual(segment2.idx, segment3.idx)
def testDestroySegmentsThenReachLimit(self):
""" Destroy some segments then verify that the maxSegmentsPerCell is still
correctly applied.
"""
connections = Connections(1024, 2, 2)
segment1 = connections.createSegment(11)
segment2 = connections.createSegment(11)
self.assertEqual(2, connections.numSegments())
connections.destroySegment(segment1)
connections.destroySegment(segment2)
self.assertEqual(0, connections.numSegments())
connections.createSegment(11)
self.assertEqual(1, connections.numSegments())
connections.createSegment(11)
self.assertEqual(2, connections.numSegments())
segment3 = connections.createSegment(11)
self.assertLess(segment3.idx, 2)
self.assertEqual(2, connections.numSegments())
def testSynapseReuse(self):
""" Creates a synapse over the synapses per segment limit, and verifies
that the lowest permanence synapse is removed to make room for the new
synapse.
"""
connections = Connections(1024, 1024, 2)
segment = connections.createSegment(10)
synapse1 = connections.createSynapse(segment, 50, .34)
synapse2 = connections.createSynapse(segment, 51, .34)
synapses = connections.synapsesForSegment(segment)
self.assertEqual(synapses, [synapse1, synapse2])
#Add an additional synapse to force it over the limit of num synapses
#per segment.
synapse3 = connections.createSynapse(segment, 52, .52)
self.assertEqual(0, synapse3.idx)
#ensure lower permanence synapse was removed
synapses = connections.synapsesForSegment(segment)
self.assertEqual(synapses, [synapse3, synapse2])
def testReuseSegmentWithDestroyedSynapses(self):
""" Destroy a segment that has a destroyed synapse and a non-destroyed
synapse. Create a new segment in the same place. Make sure its synapse
count is correct.
"""
connections = Connections(1024)
segment = connections.createSegment(11)
synapse1 = connections.createSynapse(segment, 201, .85)
connections.createSynapse(segment, 202, .85)
connections.destroySynapse(synapse1)
self.assertEqual(1, connections.numSynapses(segment))
connections.destroySegment(segment)
reincarnated = connections.createSegment(11)
self.assertEqual(0, connections.numSynapses(reincarnated))
self.assertEqual(0, len(connections.synapsesForSegment(reincarnated)))
def testDestroySynapsesThenReachLimit(self):
""" Destroy some synapses then verify that the maxSynapsesPerSegment is
still correctly applied.
"""
connections = Connections(1024, 2, 2)
segment = connections.createSegment(10)
synapse1 = connections.createSynapse(segment, 201, .85)
synapse2 = connections.createSynapse(segment, 202, .85)
self.assertEqual(2, connections.numSynapses())
connections.destroySynapse(synapse1)
connections.destroySynapse(synapse2)
self.assertEqual(0, connections.numSynapses())
connections.createSynapse(segment, 201, .85)
self.assertEqual(1, connections.numSynapses())
connections.createSynapse(segment, 202, .90)
self.assertEqual(2, connections.numSynapses())
synapse3 = connections.createSynapse(segment, 203, .8)
self.assertEqual(2, connections.numSynapses())
def testDestroySynapse(self):
""" Creates a segment, creates a number of synapses on it, destroys a
synapse, and makes sure it got destroyed.
"""
connections = Connections(1024)
segment = connections.createSegment(20)
synapse1 = connections.createSynapse(segment, 80, .85)
synapse2 = connections.createSynapse(segment, 81, .85)
synapse3 = connections.createSynapse(segment, 82, .15)
self.assertEqual(3, connections.numSynapses())
connections.destroySynapse(synapse2)
self.assertEqual(2, connections.numSynapses())
self.assertEqual(connections.synapsesForSegment(segment),
[synapse1, synapse3])
active, matching = connections.computeActivity([80, 81, 82], .5, 2,
0.0, 1)
self.assertEqual(0, len(active))
self.assertEqual(1, len(matching))
self.assertEqual(2, matching[0].overlap)
def testSynapseReuse(self):
""" Creates a synapse over the synapses per segment limit, and verifies
that the lowest permanence synapse is removed to make room for the new
synapse.
"""
connections = Connections(1024, 1024, 2)
segment = connections.createSegment(10)
synapse1 = connections.createSynapse(segment, 50, .34)
synapse2 = connections.createSynapse(segment, 51, .48)
synapses = connections.synapsesForSegment(segment)
self.assertEqual(set([synapse1, synapse2]), synapses)
# Add an additional synapse to force it over the limit of num synapses
# per segment.
connections.createSynapse(segment, 52, .52)
# Ensure lower permanence synapse was removed.
self.assertEqual(
set([51, 52]),
set(synapse.presynapticCell
for synapse in connections.synapsesForSegment(segment)))
def testDestroySynapse(self):
""" Creates a segment, creates a number of synapses on it, destroys a
synapse, and makes sure it got destroyed.
"""
connections = Connections(1024)
segment = connections.createSegment(20)
synapse1 = connections.createSynapse(segment, 80, .85)
synapse2 = connections.createSynapse(segment, 81, .85)
synapse3 = connections.createSynapse(segment, 82, .15)
self.assertEqual(3, connections.numSynapses())
connections.destroySynapse(synapse2)
self.assertEqual(2, connections.numSynapses())
self.assertEqual(set([synapse1, synapse3]),
connections.synapsesForSegment(segment))
(numActiveConnected,
numActivePotential) = connections.computeActivity([80, 81, 82], .5)
self.assertEqual(1, numActiveConnected[segment.flatIdx])
self.assertEqual(2, numActivePotential[segment.flatIdx])
def testDestroySegmentWithDestroyedSynapses(self):
""" Destroy a segment that has a destroyed synapse and a non-destroyed
synapse. Make sure nothing gets double-destroyed.
"""
connections = Connections(1024)
segment1 = connections.createSegment(11)
segment2 = connections.createSegment(12)
connections.createSynapse(segment1, 101, .85)
synapse2a = connections.createSynapse(segment2, 201, .85)
connections.createSynapse(segment2, 202, .85)
self.assertEqual(3, connections.numSynapses())
connections.destroySynapse(synapse2a)
self.assertEqual(2, connections.numSegments())
self.assertEqual(2, connections.numSynapses())
connections.destroySegment(segment2)
self.assertEqual(1, connections.numSegments())
self.assertEqual(1, connections.numSynapses())
def __init__(self,
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=255,
maxSynapsesPerSegment=255,
seed=42,
**kwargs):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected
synapses on a segment is at least
this threshold, the segment is said
to be active.
@param initialPermanence (float) Initial permanence of a new synapse
@param connectedPermanence (float) If the permanence value for a
synapse is greater than this value,
it is said to be connected.
@param minThreshold (int) If the number of synapses active on
a segment is at least this
threshold, it is selected as the
best matching cell in a bursting
column
@param maxNewSynapseCount (int) The maximum number of synapses
added to a segment during learning
@param permanenceIncrement (float) Amount by which permanences of
synapses are incremented during
learning.
@param permanenceDecrement (float) Amount by which permanences of
synapses are decremented during
learning.
@param predictedSegmentDecrement (float) Amount by which active permanences
of synapses of previously predicted
but inactive segments are
decremented.
@param seed (int) Seed for the random number
generator
Notes:
predictedSegmentDecrement: A good value is just a bit larger than
(the column-level sparsity * permanenceIncrement). So, if column-level
sparsity is 2% and permanenceIncrement is 0.01, this parameter should be
something like 4% * 0.01 = 0.0004).
"""
# Error checking
if not len(columnDimensions):
raise ValueError(
"Number of column dimensions must be greater than 0")
if cellsPerColumn <= 0:
raise ValueError(
"Number of cells per column must be greater than 0")
if minThreshold > activationThreshold:
raise ValueError(
"The min threshold can't be greater than the activation threshold"
)
# TODO: Validate all parameters (and add validation tests)
# Save member variables
self.columnDimensions = columnDimensions
self.cellsPerColumn = cellsPerColumn
self.activationThreshold = activationThreshold
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
self.predictedSegmentDecrement = predictedSegmentDecrement
# Initialize member variables
self.connections = Connections(
self.numberOfCells(),
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment)
self._random = Random(seed)
self.activeCells = []
self.winnerCells = []
self.activeSegments = []
self.matchingSegments = []
def setUp(self):
self.connections = Connections(2048 * 32)
